Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	14.0	47.404542
daal4py_KNeighborsClassifier	0.0	5.0	46.588274
KNeighborsClassifier_kd_tree	0.0	6.0	25.657507
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	55.493755
KMeans_tall	0.0	1.0	47.564210
daal4py_KMeans_tall	0.0	1.0	19.469896
KMeans_short	0.0	0.0	29.087693
daal4py_KMeans_short	0.0	0.0	15.081865
LogisticRegression	0.0	1.0	18.642543
daal4py_LogisticRegression	0.0	1.0	11.708509
Ridge	0.0	1.0	11.542230
daal4py_Ridge	0.0	0.0	28.741659
total	0.0	36.0	57.081772

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.158	0.006	1000000	1000000	100	brute	-1	1	NaN	NaN	0.575	0.007	0.275	0.001	See
1	KNeighborsClassifier	predict	0.207	0.018	1000000	1	100	brute	-1	1	1.0	0.0	0.111	0.004	1.874	0.009	See
2	KNeighborsClassifier	predict	34.988	0.000	1000000	1000	100	brute	-1	1	1.0	0.0	4.549	0.051	7.691	0.000	See
3	KNeighborsClassifier	fit	0.154	0.003	1000000	1000000	100	brute	-1	5	NaN	NaN	0.583	0.014	0.263	0.001	See
4	KNeighborsClassifier	predict	0.201	0.014	1000000	1	100	brute	-1	5	1.0	0.0	0.108	0.003	1.865	0.005	See
5	KNeighborsClassifier	predict	42.143	0.000	1000000	1000	100	brute	-1	5	1.0	0.0	4.571	0.037	9.219	0.000	See
6	KNeighborsClassifier	fit	0.153	0.003	1000000	1000000	100	brute	-1	100	NaN	NaN	0.568	0.017	0.269	0.001	See
7	KNeighborsClassifier	predict	0.204	0.018	1000000	1	100	brute	-1	100	1.0	1.0	0.111	0.007	1.836	0.011	See
8	KNeighborsClassifier	predict	42.157	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	4.638	0.030	9.089	0.000	See
9	KNeighborsClassifier	fit	0.152	0.004	1000000	1000000	100	brute	1	1	NaN	NaN	0.599	0.020	0.254	0.002	See
10	KNeighborsClassifier	predict	0.224	0.003	1000000	1	100	brute	1	1	1.0	0.0	0.107	0.001	2.089	0.000	See
11	KNeighborsClassifier	predict	18.028	0.051	1000000	1000	100	brute	1	1	1.0	0.0	4.596	0.047	3.923	0.000	See
12	KNeighborsClassifier	fit	0.154	0.006	1000000	1000000	100	brute	1	5	NaN	NaN	0.589	0.012	0.261	0.002	See
13	KNeighborsClassifier	predict	0.233	0.005	1000000	1	100	brute	1	5	1.0	0.0	0.114	0.002	2.047	0.001	See
14	KNeighborsClassifier	predict	26.298	0.036	1000000	1000	100	brute	1	5	1.0	0.0	4.665	0.109	5.638	0.001	See
15	KNeighborsClassifier	fit	0.152	0.005	1000000	1000000	100	brute	1	100	NaN	NaN	0.591	0.009	0.257	0.002	See
16	KNeighborsClassifier	predict	0.234	0.007	1000000	1	100	brute	1	100	1.0	1.0	0.110	0.003	2.139	0.002	See
17	KNeighborsClassifier	predict	26.876	0.238	1000000	1000	100	brute	1	100	1.0	1.0	4.677	0.036	5.747	0.000	See
18	KNeighborsClassifier	fit	0.055	0.002	1000000	1000000	2	brute	-1	1	NaN	NaN	0.101	0.003	0.547	0.002	See
19	KNeighborsClassifier	predict	0.021	0.003	1000000	1	2	brute	-1	1	1.0	1.0	0.005	0.000	4.485	0.025	See
20	KNeighborsClassifier	predict	25.910	0.329	1000000	1000	2	brute	-1	1	1.0	1.0	1.002	0.010	25.868	0.000	See
21	KNeighborsClassifier	fit	0.053	0.003	1000000	1000000	2	brute	-1	5	NaN	NaN	0.101	0.004	0.524	0.004	See
22	KNeighborsClassifier	predict	0.028	0.003	1000000	1	2	brute	-1	5	1.0	1.0	0.005	0.001	5.958	0.025	See
23	KNeighborsClassifier	predict	33.909	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	1.018	0.014	33.324	0.000	See
24	KNeighborsClassifier	fit	0.055	0.004	1000000	1000000	2	brute	-1	100	NaN	NaN	0.102	0.005	0.538	0.008	See
25	KNeighborsClassifier	predict	0.026	0.002	1000000	1	2	brute	-1	100	1.0	1.0	0.006	0.001	4.371	0.031	See
26	KNeighborsClassifier	predict	33.089	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	1.089	0.012	30.388	0.000	See
27	KNeighborsClassifier	fit	0.054	0.002	1000000	1000000	2	brute	1	1	NaN	NaN	0.105	0.005	0.509	0.003	See
28	KNeighborsClassifier	predict	0.013	0.001	1000000	1	2	brute	1	1	1.0	1.0	0.005	0.001	2.601	0.029	See
29	KNeighborsClassifier	predict	11.825	0.108	1000000	1000	2	brute	1	1	1.0	1.0	1.021	0.014	11.580	0.000	See
30	KNeighborsClassifier	fit	0.069	0.002	1000000	1000000	2	brute	1	5	NaN	NaN	0.108	0.003	0.639	0.002	See
31	KNeighborsClassifier	predict	0.020	0.002	1000000	1	2	brute	1	5	1.0	1.0	0.005	0.001	3.843	0.019	See
32	KNeighborsClassifier	predict	21.789	0.301	1000000	1000	2	brute	1	5	1.0	1.0	1.018	0.012	21.397	0.000	See
33	KNeighborsClassifier	fit	0.073	0.005	1000000	1000000	2	brute	1	100	NaN	NaN	0.100	0.008	0.729	0.010	See
34	KNeighborsClassifier	predict	0.021	0.002	1000000	1	2	brute	1	100	1.0	1.0	0.005	0.000	4.268	0.013	See
35	KNeighborsClassifier	predict	20.646	0.019	1000000	1000	2	brute	1	100	1.0	1.0	1.096	0.022	18.841	0.000	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.853	0.116	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.790	0.019	3.613	0.002	See
1	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.001	0.000	5.005	0.393	See
2	KNeighborsClassifier_kd_tree	predict	0.441	0.008	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.120	0.001	3.680	0.000	See
3	KNeighborsClassifier_kd_tree	fit	2.858	0.164	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.814	0.012	3.512	0.003	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.001	0.000	4.181	0.161	See
5	KNeighborsClassifier_kd_tree	predict	0.923	0.020	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.220	0.006	4.195	0.001	See
6	KNeighborsClassifier_kd_tree	fit	2.774	0.106	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.771	0.013	3.596	0.002	See
7	KNeighborsClassifier_kd_tree	predict	0.005	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.002	0.001	2.699	0.166	See
8	KNeighborsClassifier_kd_tree	predict	3.005	0.036	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.669	0.009	4.490	0.000	See
9	KNeighborsClassifier_kd_tree	fit	2.679	0.037	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.820	0.012	3.265	0.000	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	10	kd_tree	1	1	1.0	1.0	0.001	0.000	1.950	0.370	See
11	KNeighborsClassifier_kd_tree	predict	0.756	0.015	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.122	0.002	6.200	0.001	See
12	KNeighborsClassifier_kd_tree	fit	2.729	0.092	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.784	0.009	3.483	0.001	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	10	kd_tree	1	5	1.0	1.0	0.001	0.000	2.022	0.599	See
14	KNeighborsClassifier_kd_tree	predict	1.446	0.037	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.234	0.007	6.188	0.002	See
15	KNeighborsClassifier_kd_tree	fit	2.606	0.067	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.864	0.052	3.016	0.004	See
16	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.002	0.001	1.902	0.286	See
17	KNeighborsClassifier_kd_tree	predict	4.833	0.131	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.706	0.024	6.848	0.002	See
18	KNeighborsClassifier_kd_tree	fit	1.264	0.027	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.566	0.016	2.231	0.001	See
19	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	10.798	1.262	See
20	KNeighborsClassifier_kd_tree	predict	0.038	0.002	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	38.293	0.170	See
21	KNeighborsClassifier_kd_tree	fit	1.262	0.032	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.577	0.021	2.186	0.002	See
22	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	10.894	0.467	See
23	KNeighborsClassifier_kd_tree	predict	0.036	0.002	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.002	0.001	20.607	0.462	See
24	KNeighborsClassifier_kd_tree	fit	1.262	0.061	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.578	0.019	2.184	0.003	See
25	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	10.625	0.261	See
26	KNeighborsClassifier_kd_tree	predict	0.054	0.003	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.008	0.001	6.785	0.011	See
27	KNeighborsClassifier_kd_tree	fit	1.231	0.030	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.588	0.061	2.094	0.012	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.690	0.805	See
29	KNeighborsClassifier_kd_tree	predict	0.034	0.003	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	34.042	0.073	See
30	KNeighborsClassifier_kd_tree	fit	1.272	0.024	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.547	0.013	2.324	0.001	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	3.806	0.309	See
32	KNeighborsClassifier_kd_tree	predict	0.041	0.003	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.002	0.001	26.883	0.121	See
33	KNeighborsClassifier_kd_tree	fit	1.291	0.044	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.552	0.009	2.337	0.001	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	4.451	0.117	See
35	KNeighborsClassifier_kd_tree	predict	0.072	0.004	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.008	0.001	8.735	0.010	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.696	0.011	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.359	0.009	1.940	0.001	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.122	0.360	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.834	0.294	See
3	KMeans_tall	fit	0.607	0.024	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.312	0.012	1.945	0.003	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.989	0.463	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.840	0.449	See
6	KMeans_tall	fit	8.818	0.105	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	4.517	0.064	1.952	0.000	See
7	KMeans_tall	predict	0.001	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.939	0.512	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.935	0.240	See
9	KMeans_tall	fit	7.884	0.083	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	4.308	0.058	1.830	0.000	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.022	0.604	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.221	0.197	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.448	0.023	10000	10000	2	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	0.181	0.008	2.481	0.004	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.091	0.275	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.244	0.117	See
3	KMeans_short	fit	0.160	0.006	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.082	0.002	1.940	0.002	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.833	0.291	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.117	0.047	See
6	KMeans_short	fit	1.406	0.052	10000	10000	100	full	k-means++	30	300	1	0.0	19.0	NaN	17.0	NaN	0.694	0.040	2.025	0.005	See
7	KMeans_short	predict	0.001	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.180	0.420	See
8	KMeans_short	predict	0.008	0.002	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	4.610	0.136	See
9	KMeans_short	fit	0.408	0.056	10000	10000	100	full	random	30	300	1	0.0	19.0	NaN	24.0	NaN	0.373	0.050	1.094	0.037	See
10	KMeans_short	predict	0.001	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.824	0.327	See
11	KMeans_short	predict	0.010	0.001	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	6.064	0.036	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	17.587	0.033	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	17.584	0.127	1.000	0.000	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.392	0.784	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.000	0.953	0.281	See
3	LogisticRegression	fit	1.447	0.042	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	1.525	0.035	0.949	0.001	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.000	0.161	0.237	See
5	LogisticRegression	predict	0.003	0.001	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.004	0.000	0.818	0.083	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	3.246	0.070	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.927	0.026	1.684	0.001	See
1	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	1.581	2.908	See
2	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.001	0.000	0.749	0.129	See
3	Ridge	fit	1.714	0.043	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.430	0.007	3.991	0.001	See
4	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.492	0.529	See
5	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.701	0.306	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}